Machine learning has rapidly matured over the years, and is the norm in a number of fields, helping companies deploy smart systems of engagement to improve efficiency, enhance security, gain insights and deliver superior customer experiences, says Aan Chauhan.
“Be it virtual cognitive agents delivering contextual and personalized services and customer interactions, predictive analytics engines that help companies forecast, systems that help automate business processes, applications that automate infrastructure management and application services, or deep learning systems that augment human expert capabilities, machine learning applications are being used across industries,” says Chauhan.
Examples include robo-advisors in financial services, sales forecasting in retail, supply chain optimization in logistics, robotic process automation and even medical image analysis, such as screening images of the retina for diabetic retinopathy.
“AI and machine learning platforms take a while to ‘learn’, but the effectiveness of the engine improves with time,” says Chauhan.
He notes that rapid adoption of machine learning has also brought some of its limitations to the fore. These include the presence of data in silos, limited availability of deep data analytics skills, varying accuracies of algorithms and the speed at which things are changing.
As technology matures, Chauhan warns that an over-reliance on machine learning or a misunderstanding of its abilities could have significant consequences, especially since machine learning-based applications might not be able to fully comprehend peculiarities of human sentiments and cultural contexts right at the outset.
“Businesses must realize that machine learning is primarily designed to help employees get better at what they do, and not as a tool to replace people,” he says.
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